COMMON STATISTICAL ERRORS AND THEIR PREVENTION

By P. NOVAA

PART I FOUNDATIONS 1 SOURCES OF ERROR PRESCRIPTION FUNDAMENTAL CONCEPTS AD HOC, POST HOC HYPOTHESES 2 HYPOTHESES: THE WHY OF YOUR RESEARCH WHAT IS A HYPOTHESIS? NULL HYPOTHESIS NEYMAN–PEARSON THEORY DEDUCTION AND INDUCTION LOSSES DECISIONS TO LEARN MORE 3 COLLECTING DATA PREPARATION MEASURING DEVICES DETERMINING SAMPLE SIZE FUNDAMENTAL ASSUMPTIONS EXPERIMENTAL DESIGN FOUR GUIDELINES CONTENTS VI CONTENTS PART II HYPOTHESIS TESTING AND ESTIMATION 4 ESTIMATION PREVENTION DESIRABLE AND NOT-SO-DESIRABLE ESTIMATORS INTERVAL ESTIMATES IMPROVED RESULTS SUMMARY 5 TESTING HYPOTHESES: CHOOSING A TEST STATISTIC COMPARING MEANS OF TWO POPULATIONS COMPARING VARIANCES COMPARING THE MEANS OF K SAMPLES HIGHER-ORDER EXPERIMENTAL DESIGNS CONTINGENCY TABLES INFERIOR TESTS MULTIPLE TESTS BEFORE YOU DRAW CONCLUSIONS 6 STRENGTHS AND LIMITATIONS OF SOME MISCELLANEOUS STATISTICAL PROCEDURES BOOTSTRAP BAYESIAN METHODOLOGY META-ANALYSIS PERMUTATION TESTS 7 REPORTING YOUR RESULTS FUNDAMENTALS TABLES STANDARD ERROR P VALUES CON?DENCE INTERVALS RECOGNIZING AND REPORTING BIASES REPORTING POWER DRAWING CONCLUSIONS 8 GRAPHICS THE SOCCER DATA FIVE RULES FOR AVOIDING BAD GRAPHICS ONE RULE FOR CORRECT USAGE OF THREE-DIMENSIONAL GRAPHICS ONE RULE FOR THE MISUNDERSTOOD PIE CHART THREE RULES FOR EFFECTIVE DISPLAY OF SUBGROUP INFORMATION TWO RULES FOR TEXT ELEMENTS IN GRAPHICS MULTIDIMENSIONAL DISPLAYS CHOOSING EFFECTIVE DISPLAY ELEMENTS CHOOSING GRAPHICAL DISPLAYS PART III BUILDING A MODEL 9 UNIVARIATE REGRESSION MODEL SELECTION ESTIMATING COEF?CIENTS FURTHER CONSIDERATIONS 10 MULTIVARIABLE REGRESSION GENERALIZED LINEAR MODELS REPORTING YOUR RESULTS A CONJECTURE BUILDING A SUCCESSFUL MODEL 11 VALIDATION METHODS OF VALIDATION MEASURES OF PREDICTIVE SUCCESS LONG-TERM STABILITY